Modeling of Global and Individual Kinetic Parameters in Wheat Straw Torrefaction: Particle Swarm Optimization and Its Impact on Elemental Composition Prediction

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Abstract
With the growing demand for sustainable energy solutions, biomass torrefaction has emerged as a crucial technology for converting agricultural waste into high-value biofuels. This work develops dual kinetic modeling using global and individual parameters combined using particle swarm optimization (PSO) to predict energy densification based on elemental composition (CHNO) and high heating values (HHVs). The global parameters are calculated from experiments conducted at 250 °C, 275 °C, and 300 °C, and the individual parameters are obtained by adjusting experimental points at each temperature. A two-step kinetic model was used and optimized to achieve exceptional adjustment accuracy (98.073–99.999%). The experiments were carried out in an inert atmosphere of nitrogen with a heating rate of 20 °C/min and a 100 min residence time. The results obtained demonstrate a crucial trade-off: while individual parameters provide superior accuracy (an average fit of 99.516%) for predicting degradation by weight loss, global parameters offer better predictions for elemental composition, with average errors of 2.129% (carbon), 1.038% (hydrogen), 9.540% (nitrogen), and 3.997% (oxygen). Furthermore, it has been found that by determining the kinetic parameters at a torrefaction temperature higher than the maximum peak observed in the derivative thermogravimetric (DTG) curve (275 °C), it is possible to predict the behavior of the process within the 250–325 °C range with an R-squared value corresponding to an error lower than 3%. This approach significantly reduces the number of required experiments from twelve to only four by relying on a single isothermal condition for parameter estimation.
Funding Information
  • Secretaría de Ciencia, Humanidades, Tecnología e Innovación (843043)

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